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Unpin numpy (#29421)

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* Unpin numpy

* Update lock file

* leave acados comment

* Fix warnings

* Fix more paramsd warnings
pull/29457/head
Kacper Rączy 2 years ago committed by GitHub
parent b8b1e17a21
commit 62a88e504d
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4 changed files with 44 additions and 47 deletions
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  1. 61
      poetry.lock
  2. 2
      pyproject.toml
  3. 2
      selfdrive/controls/lib/lateral_planner.py
  4. 26
      selfdrive/locationd/paramsd.py

61
poetry.lock generated
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@ -2178,39 +2178,36 @@ setuptools = "*"
[[package]] [[package]]
name = "numpy" name = "numpy"
version = "1.23.5" version = "1.25.2"
description = "NumPy is the fundamental package for array computing with Python." description = "Fundamental package for array computing in Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.9"
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{file = "numpy-1.23.5.tar.gz", hash = "sha256:1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a"},
] ]
[[package]] [[package]]
@ -4164,4 +4161,4 @@ multidict = ">=4.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "~3.11" python-versions = "~3.11"
content-hash = "849f9d091efd79cb02fffa24846ddb1dd2857cfdce45527093e3bef7d0bc3598" content-hash = "4b2510f1465520a9dc757f64861f4fcea9d9663c0ecdebd23b329ef5e0205863"

2
pyproject.toml
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@ -31,7 +31,7 @@ hexdump = "*"
Jinja2 = "*" Jinja2 = "*"
json-rpc = "*" json-rpc = "*"
libusb1 = "*" libusb1 = "*"
numpy = "~1.23" # pinned for acados numpy = "*"
onnx = ">=1.14.0" onnx = ">=1.14.0"
onnxruntime-gpu = { version = ">=1.15.1", platform = "linux", markers = "platform_machine == 'x86_64'" } onnxruntime-gpu = { version = ">=1.15.1", platform = "linux", markers = "platform_machine == 'x86_64'" }
pillow = "*" pillow = "*"

2
selfdrive/controls/lib/lateral_planner.py
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@ -132,7 +132,7 @@ class LateralPlanner:
lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist() lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3]/self.v_ego).tolist() lateralPlan.curvatures = (self.lat_mpc.x_sol[0:CONTROL_N, 3]/self.v_ego).tolist()
lateralPlan.curvatureRates = [float(x/self.v_ego) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0] lateralPlan.curvatureRates = [float(x.item() / self.v_ego) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
lateralPlan.mpcSolutionValid = bool(plan_solution_valid) lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
lateralPlan.solverExecutionTime = self.lat_mpc.solve_time lateralPlan.solverExecutionTime = self.lat_mpc.solve_time

26
selfdrive/locationd/paramsd.py
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@ -85,8 +85,8 @@ class ParamsLearner:
# We observe the current stiffness and steer ratio (with a high observation noise) to bound # We observe the current stiffness and steer ratio (with a high observation noise) to bound
# the respective estimate STD. Otherwise the STDs keep increasing, causing rapid changes in the # the respective estimate STD. Otherwise the STDs keep increasing, causing rapid changes in the
# states in longer routes (especially straight stretches). # states in longer routes (especially straight stretches).
stiffness = float(self.kf.x[States.STIFFNESS]) stiffness = float(self.kf.x[States.STIFFNESS].item())
steer_ratio = float(self.kf.x[States.STEER_RATIO]) steer_ratio = float(self.kf.x[States.STEER_RATIO].item())
self.kf.predict_and_observe(t, ObservationKind.STIFFNESS, np.array([[stiffness]])) self.kf.predict_and_observe(t, ObservationKind.STIFFNESS, np.array([[stiffness]]))
self.kf.predict_and_observe(t, ObservationKind.STEER_RATIO, np.array([[steer_ratio]])) self.kf.predict_and_observe(t, ObservationKind.STEER_RATIO, np.array([[steer_ratio]]))
@ -198,14 +198,14 @@ def main(sm=None, pm=None):
learner = ParamsLearner(CP, CP.steerRatio, 1.0, 0.0) learner = ParamsLearner(CP, CP.steerRatio, 1.0, 0.0)
x = learner.kf.x x = learner.kf.x
angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET]), angle_offset_average = clip(math.degrees(x[States.ANGLE_OFFSET].item()),
angle_offset_average - MAX_ANGLE_OFFSET_DELTA, angle_offset_average + MAX_ANGLE_OFFSET_DELTA) angle_offset_average - MAX_ANGLE_OFFSET_DELTA, angle_offset_average + MAX_ANGLE_OFFSET_DELTA)
angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET] + x[States.ANGLE_OFFSET_FAST]), angle_offset = clip(math.degrees(x[States.ANGLE_OFFSET].item() + x[States.ANGLE_OFFSET_FAST].item()),
angle_offset - MAX_ANGLE_OFFSET_DELTA, angle_offset + MAX_ANGLE_OFFSET_DELTA) angle_offset - MAX_ANGLE_OFFSET_DELTA, angle_offset + MAX_ANGLE_OFFSET_DELTA)
roll = clip(float(x[States.ROAD_ROLL]), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA) roll = clip(float(x[States.ROAD_ROLL].item()), roll - ROLL_MAX_DELTA, roll + ROLL_MAX_DELTA)
roll_std = float(P[States.ROAD_ROLL]) roll_std = float(P[States.ROAD_ROLL].item())
# Account for the opposite signs of the yaw rates # Account for the opposite signs of the yaw rates
sensors_valid = bool(abs(learner.speed * (x[States.YAW_RATE] + learner.yaw_rate)) < LATERAL_ACC_SENSOR_THRESHOLD) sensors_valid = bool(abs(learner.speed * (x[States.YAW_RATE].item() + learner.yaw_rate)) < LATERAL_ACC_SENSOR_THRESHOLD)
avg_offset_valid = check_valid_with_hysteresis(avg_offset_valid, angle_offset_average, OFFSET_MAX, OFFSET_LOWERED_MAX) avg_offset_valid = check_valid_with_hysteresis(avg_offset_valid, angle_offset_average, OFFSET_MAX, OFFSET_LOWERED_MAX)
total_offset_valid = check_valid_with_hysteresis(total_offset_valid, angle_offset, OFFSET_MAX, OFFSET_LOWERED_MAX) total_offset_valid = check_valid_with_hysteresis(total_offset_valid, angle_offset, OFFSET_MAX, OFFSET_LOWERED_MAX)
roll_valid = check_valid_with_hysteresis(roll_valid, roll, ROLL_MAX, ROLL_LOWERED_MAX) roll_valid = check_valid_with_hysteresis(roll_valid, roll, ROLL_MAX, ROLL_LOWERED_MAX)
@ -215,8 +215,8 @@ def main(sm=None, pm=None):
liveParameters = msg.liveParameters liveParameters = msg.liveParameters
liveParameters.posenetValid = True liveParameters.posenetValid = True
liveParameters.sensorValid = sensors_valid liveParameters.sensorValid = sensors_valid
liveParameters.steerRatio = float(x[States.STEER_RATIO]) liveParameters.steerRatio = float(x[States.STEER_RATIO].item())
liveParameters.stiffnessFactor = float(x[States.STIFFNESS]) liveParameters.stiffnessFactor = float(x[States.STIFFNESS].item())
liveParameters.roll = roll liveParameters.roll = roll
liveParameters.angleOffsetAverageDeg = angle_offset_average liveParameters.angleOffsetAverageDeg = angle_offset_average
liveParameters.angleOffsetDeg = angle_offset liveParameters.angleOffsetDeg = angle_offset
@ -228,10 +228,10 @@ def main(sm=None, pm=None):
0.2 <= liveParameters.stiffnessFactor <= 5.0, 0.2 <= liveParameters.stiffnessFactor <= 5.0,
min_sr <= liveParameters.steerRatio <= max_sr, min_sr <= liveParameters.steerRatio <= max_sr,
)) ))
liveParameters.steerRatioStd = float(P[States.STEER_RATIO]) liveParameters.steerRatioStd = float(P[States.STEER_RATIO].item())
liveParameters.stiffnessFactorStd = float(P[States.STIFFNESS]) liveParameters.stiffnessFactorStd = float(P[States.STIFFNESS].item())
liveParameters.angleOffsetAverageStd = float(P[States.ANGLE_OFFSET]) liveParameters.angleOffsetAverageStd = float(P[States.ANGLE_OFFSET].item())
liveParameters.angleOffsetFastStd = float(P[States.ANGLE_OFFSET_FAST]) liveParameters.angleOffsetFastStd = float(P[States.ANGLE_OFFSET_FAST].item())
if DEBUG: if DEBUG:
liveParameters.filterState = log.LiveLocationKalman.Measurement.new_message() liveParameters.filterState = log.LiveLocationKalman.Measurement.new_message()
liveParameters.filterState.value = x.tolist() liveParameters.filterState.value = x.tolist()

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